Smart diagnosis: Efficient board-level diagnosis and repair using artificial neural networks

Zhang, Zhaobo; Chakrabarty, Krishnendu; Wang, Zhanglei; Wang, Zhiyuan; Gu, Xinli

doi:10.1109/test.2011.6139139

Cited by 28 publications

(10 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To alleviate the difficulty of model construction and maintenance, machine learning and data analysis tools such as neural networks [15,22], bayesian inference [14] and supportvector machine [23] are employed to construct a reasoning engine for diagnosis. By training the system with historical diagnosis data, it automatically infers the root cause of a new failed board given its test syndromes.…”

Section: Related Work and Motivationmentioning

confidence: 99%

AgentDiag: An agent-assisted diagnostic framework for board-level functional failures

Sun

Jiang

et al. 2013

2013 IEEE International Test Conference (ITC)

Self Cite

View full text Add to dashboard Cite

Diagnosing functional failures in complicated electronic boards is a challenging task, wherein debug technicians try to identify defective components by analyzing some syndromes obtained from the application of diagnostic tests. The diagnosis effectiveness and efficiency rely heavily on the quality of the in-house developed diagnostic tests and the debug technicians' knowledge and experience, which, however, have no guarantees nowadays. To tackle this problem, we propose a novel agent-assisted diagnostic framework for boardlevel functional failures, namely AgentDiag, which facilitates to evaluate the quality of the diagnostic tests and bridge the knowledge gap between the diagnostic programmers who write diagnostic tests and the debug technicians who conduct in-field diagnosis with a lightweight model of the boards and tests. Experimental results on a real industrial board and an OpenRISC design demonstrate the effectiveness of the proposed solution.

show abstract

Section: Related Work and Motivationmentioning

confidence: 99%

AgentDiag: An agent-assisted diagnostic framework for board-level functional failures

Sun

Jiang

et al. 2013

2013 IEEE International Test Conference (ITC)

Self Cite

View full text Add to dashboard Cite

show abstract

“…An advantage of using machine learning is that it avoids the difficulties associated with knowledge acquisition and rule-base development required for expert systems [16], [17]. Without the need to understand the complex functionality of boards, diagnostic systems based on machine learning are able to automatically derive and exploit knowledge from repair logs of previously documented cases.…”

Section: Problem Statement and Paper Contributionsmentioning

confidence: 99%

“…The final repair suggestion is available at the leaf node of a DT. The diagnosis time can be significantly reduced using DTs, since only a small number of syndromes are needed for identifying the root cause, instead of all the syndromes that are used in ANNs and SVMs [16], [17]. As shown in Section V for industry boards, only tens of syndromes on average are required for identifying the root cause.…”

Section: Problem Statement and Paper Contributionsmentioning

confidence: 99%

“…Machine-learning techniques have also been developed for accurate diagnosis. Artificial neural networks (ANNs) [16] and support-vector machines (SVMs) [17] have been used to achieve high diagnosis accuracy. However, the number of syndromes needed for training and diagnosis in ANNs and SVMs are often prohibitively high due to the complexity of today's boards.…”

Section: Introductionmentioning

confidence: 99%

“…However, the number of syndromes needed for training and diagnosis in ANNs and SVMs are often prohibitively high due to the complexity of today's boards. Diagnosis logs for functional tests record hundreds and thousands of syndromes in [16], [17], but many of these are redundant and useless for discriminating one fault from another. Nevertheless, it is a nontrivial task to identify the syndromes that are useful.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Adaptive Board-Level Functional Fault Diagnosis Using Decision Trees

Zhang

Chakrabarty

et al. 2012

2012 IEEE 21st Asian Test Symposium

Self Cite

View full text Add to dashboard Cite

Functional fault diagnosis at board-level is desirable for high-volume production since it improves product yield. However, to ensure diagnosis accuracy and effective board repair, a large number of syndromes must be used. Therefore, the diagnosis cost can be prohibitively high due to the increase in diagnosis time and the complexity of syndrome collection/analysis. We propose an adaptive diagnosis method based on decision trees (DTs). Faulty components are classified according to the discriminative ability of the syndromes in DT training. The diagnosis procedure is constructed as a binary tree, with the most discriminative syndrome as the root and final repair suggestions are available as the leaf nodes of the tree. The syndrome to be collected in the next step is determined based on the observations of syndromes collected thus far in the diagnosis procedure. The number of syndromes required for diagnosis can also be significantly reduced compared to the number of syndromes used for system training. Diagnosis results for two complex boards from industry, currently in volume production, and additional synthetic data highlight the effectiveness of the proposed approach.

show abstract

A survey of digital circuit testing in the light of machine learning

Pradhan

Bhattacharya

2020

WIREs Data Min & Knowl

View full text Add to dashboard Cite

The insistent trend in today's nanoscale technology, to keep abreast of the Moore's law, has been continually opening up newer challenges to circuit designers. With rapid downscaling of integration, the intricacies involved in the manufacturing process have escalated significantly. Concomitantly, the nature of defects in silicon chips has become more complex and unpredictable, adding further difficulty in circuit testing and diagnosis. The volume of test data has surged and the parameters that govern testing of integrated circuits have increased not only in dimension but also in the complexity of their correlation. Evidently, the current scenario serves as a pertinent platform to explore new test solutions based on machine learning. In this survey, we look at various recent advances in this evolving domain in the context of digital logic testing and diagnosis. This article is categorized under: Algorithmic Development > Structure Discovery Technologies > Machine Learning Technologies > Prediction

show abstract

Smart diagnosis: Efficient board-level diagnosis and repair using artificial neural networks

Cited by 28 publications

References 14 publications

AgentDiag: An agent-assisted diagnostic framework for board-level functional failures

AgentDiag: An agent-assisted diagnostic framework for board-level functional failures

Adaptive Board-Level Functional Fault Diagnosis Using Decision Trees

A survey of digital circuit testing in the light of machine learning

Contact Info

Product

Resources

About